Titlebook: Recommender Systems Handbook; Francesco Ricci,Lior Rokach,Bracha Shapira Book 20152nd edition Springer Science+Business Media New York 201

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Recommender Systems: Introduction and Challenges,In this introductory chapter, we briefly discuss basic RS ideas and concepts. Our main goal is to delineate, in a coherent and structured way, the chapters included in this handbook. Additionally, we aim to help the reader navigate the rich and detailed content that this handbook offers.

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A Comprehensive Survey of Neighborhood-Based Recommendation Methodseir efficiency, and their ability to produce accurate and personalized recommendations. This chapter presents a comprehensive survey of neighborhood-based methods for the item recommendation problem. In particular, the main benefits of such methods, as well as their principal characteristics, are de

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Advances in Collaborative Filteringin the recently completed Netflix competition has contributed to its popularity. This chapter surveys the recent progress in the field. Matrix factorization techniques, which became a first choice for implementing CF, are described together with recent innovations. We also describe several extension

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Semantics-Aware Content-Based Recommender Systemsectively exploited to suggest items similar to those a target user already liked in the past. Most content-based recommender systems use textual features to represent items and user profiles, hence they suffer from the classical problems of natural language ambiguity. This chapter presents a compreh

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Context-Aware Recommender Systemsn, information retrieval, ubiquitous and mobile computing, data mining, marketing, and management. While a substantial amount of research has already been performed in the area of recommender systems, many existing approaches focus on recommending the most relevant items to users without taking into

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Evaluating Recommender Systemsses a system designer that wishes to employ a recommendater system must choose between a set of candidate approaches. A first step towards selecting an appropriate algorithm is to decide which properties of the application to focus upon when making this choice. Indeed, recommender systems have a var

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